Using Community Detection Techniques to Identify Themes in COVID-19-Related Patient Safety Event Reports. (Record no. 264)

MARC details
000 -LEADER
fixed length control field 03142nam a22004097a 4500
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fixed length control field 221018s20222022 xxu||||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1549-8417
024 ## - OTHER STANDARD IDENTIFIER
Standard number or code 01209203-990000000-00070 [pii]
024 ## - OTHER STANDARD IDENTIFIER
Standard number or code 10.1097/PTS.0000000000001051 [doi]
040 ## - CATALOGING SOURCE
Original cataloging agency Ovid MEDLINE(R)
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
PMID 36112536
245 ## - TITLE STATEMENT
Title Using Community Detection Techniques to Identify Themes in COVID-19-Related Patient Safety Event Reports.
251 ## - Source
Source Journal of patient safety. 2022 Sep 08
252 ## - Abbreviated Source
Abbreviated source J Patient Saf. 2022 Sep 08
253 ## - Journal Name
Journal name Journal of patient safety
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Year 2022
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Manufacturer FY2023
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Publication date 2022 Sep 08
265 ## - SOURCE FOR ACQUISITION/SUBSCRIPTION ADDRESS [OBSOLETE]
Publication status aheadofprint
266 ## - Date added to catalog
Date added to catalog 2022-10-20
520 ## - SUMMARY, ETC.
Abstract CONCLUSIONS: Our study begins the long process of understanding new challenges created by the pandemic and highlights how machine learning methods can be used to understand these and similar challenges. Using community detection techniques to analyze PSE reports and identify themes within them can help give healthcare systems the necessary information to improve patient safety and the quality of care they deliver. Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
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Abstract METHODS: We used community detection techniques to group 2082 PSE reports from January 1, 2020, to January 31, 2021, that mentioned COVID-19 into 65 communities. We then grouped these communities into 8 clinically relevant themes for analysis.
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Abstract OBJECTIVES: The COVID-19 pandemic has transformed how healthcare is delivered to patients. As the pandemic progresses and healthcare systems continue to adapt, it is important to understand how these changes in care have changed patient care. This study aims to use community detection techniques to identify and facilitate analysis of themes in patient safety event (PSE) reports to better understand COVID-19 pandemic's impact on patient safety. With this approach, we also seek to understand how community detection techniques can be used to better identify themes and extract information from PSE reports.
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Abstract RESULTS: We found the COVID-19 pandemic is associated with the following clinically relevant themes: (1) errors due to new and unknown COVID-19 protocols/workflows; (2) COVID-19 patients developing pressure ulcers; (3) unsuccessful/incomplete COVID-19 testing; (4) inadequate isolation of COVID-19 patients; (5) inappropriate/inadequate care for COVID-19 patients; (6) COVID-19 patient falls; (7) delays or errors communicating COVID-19 test results; and (8) COVID-19 patients developing venous thromboembolism.
546 ## - LANGUAGE NOTE
Language note English
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element IN PROCESS -- NOT YET INDEXED
651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME
Institution MedStar Institute for Innovation
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Department National Center for Human Factors in Healthcare
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Medline publication type Journal Article
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Local Authors Boxley, Christian
Institution Code NCHF
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Local Authors Fong, Allan
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Local Authors Krevat, Seth
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Local Authors Ratwani, Raj
790 ## - Authors
All authors Boxley C, Fong A, Krevat S, Ratwani R, Sengupta S
856 ## - ELECTRONIC LOCATION AND ACCESS
DOI <a href="https://dx.doi.org/10.1097/PTS.0000000000001051">https://dx.doi.org/10.1097/PTS.0000000000001051</a>
Public note https://dx.doi.org/10.1097/PTS.0000000000001051
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Journal Article
Item type description Article
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection Home library Current library Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
          MedStar Authors Catalog MedStar Authors Catalog 10/20/2022   36112536 36112536 10/20/2022 10/20/2022 Journal Article

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